Student Perceptions about Online Collaborative Coursework
Bibliographic record
Abstract
Objective: Collaborative coursework may improve student engagement but is often viewed as problematic by both students and faculty, particularly in the online classroom. The aim of this research is to present results of a retrospective, qualitative content analysis of student related perceptions about group work in the online classroom. Methods: Data analysis was completed with the use of qualitative content analysis (QCA), a valid research method for describing the meaning of qualitative data in a systematic way. QCA was used to inform the following research question: What perceptions do students have about working with a group in the online classroom? Data were collected through a retrospective analysis of student responses posted to discussion board forums. Responses of students (N = 192) enrolled in three different courses, over two semesters were analyzed by a team of two researchers. Results: Findings included student reflections about group work being a stressful, negative experience, with the asynchronous environment of the online classroom increasing student anxiety about group work. Students reported different academic goals and lack of participation among group members as common issues. In addition, students reported concern with group management or organization and the fairness of group work grading practices. Conclusions: These results inform a discussion of best practices, skills and technology faculty can use to transform online group work into a positive learning experience for all students. Online education needs to be meaningful and responsive to meet students’ needs. Research has shown group work can improve student engagement and facilitate accomplishment; however, the negotiation of group work processes can be stressful for students and faculty, particularly in the online classroom.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".